Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Machine Learning
Latent linkage semantic kernels for collective classification of link data
Journal of Intelligent Information Systems
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Classifying relational data with neural networks
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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In order to exploit the dependencies in relational data to improve predictions, relational classification models often need to make simultaneous statistical judgments about the class labels for a set of related objects. Robustness has always been an important concern for such collective classification models since many real-world relational data such as Web pages are often accompanied with much noisy information. In this paper, we propose a contextual dependency network (CDN) model for classifying linked objects in the presence of noisy and irrelevant links. The CDN model makes use of a dependency function to characterize the contextual dependencies among linked objects so that it can effectively reduce the effect of irrelevant links on the classification. We show how to use the Gibbs inference framework over the CDN model for collective classification of multiple linked objects. The experiments show that the CDN model demonstrates relatively high robustness on datasets containing irrelevant links.